OpenRaiser/Intern-Atlas huggingface dataset:
IoT-based Emergency Evacuation Systems
Venue: arXiv cs.SE 2020
Year: 2020

Fires, earthquakes, floods, hurricanes, overcrowding, or and even pandemic viruses endanger human lives. Hence, designing infrastructures to handle possible emergencies has become an ever-increasing need. The safe evacuation of occupants from the building takes precedence when dealing with the necessary mitigation and disaster risk management. This thesis deals with designing an IoT system to provide safe and quick evacuation suggestions. The IoT-based evacuation system provides optimal evacuation paths that can be continuously updated based on run-time sensory data, so evacuation guidelines can be adjusted according to visitors occupants that evolve over time. This thesis makes the following main contributions: i) Addressing an up to date state of the art class for IoT architectural styles and patterns; ii) Proposing a set of self-adaptive IoT patterns and assessing their specific quality attributes (fault-tolerance, energy consumption, and performance); iii) Designing an IoT infrastructure and testing its performance in both real-time and design-time applications; iv) Developing a network flow algorithm that facilitates minimizing the time necessary to evacuate people from a scene of a disaster; v) Modeling various social agents and their interactions during an emergency to improve the IoT system accordingly; vi) Evaluating the system by using empirical and real case studies.
Security Improvements of Several Basic Quantum Private Query Protocols with O(log N) Communication Complexity
Venue: arXiv cs.CR 2020
Year: 2020

New quantum private database (with N elements) query protocols are presented and analyzed. Protocols preserve O(logN) communication complexity of known protocols for the same task, but achieve several significant improvements in security, especially concerning user privacy. For example, the randomized form of our protocol has a cheat-sensitive property - it allows the user to detect a dishonest database with a nonzero probability, while the phase-encoded private query protocols for the same task do not have such a property. Moreover, when the database performs the computational basis measurement, a particular projective measurement which can cause a significant loss of user privacy in the previous private query protocols with O(logN) communication complexity, at most half of the user privacy could leak to such a database in our protocol, while in the QPQ protocol, the entire user privacy could leak out. In addition, it is proved here that for large N, the user could detect a cheating via the computational basis measurement, with a probability close to 1/2 using O(\sqrt{N}) special queries. Finally, it is shown here, for both forms of our protocol, basic and randomized, how a dishonest database has to act in case it could not learn user's queries.
Semi-Submersible Wind Turbine Hull Shape Design for a Favorable System Response Behavior
Venue: arXiv cs.CE 2020
Year: 2020

Floating offshore wind turbines are a novel technology, which has reached, with the first wind farm in operation, an advanced state of development. The question of how floating wind systems can be optimized to operate smoothly in harsh wind and wave conditions is the subject of the present work. An integrated optimization was conducted, where the hull shape of a semi-submersible, as well as the wind turbine controller were varied with the goal of finding a cost-efficient design, which does not respond to wind and wave excitations, resulting in small structural fatigue and extreme loads. The optimum design was found to have a remarkably low tower-base fatigue load response and small rotor fore-aft amplitudes. Further investigations showed that the reason for the good dynamic behavior is a particularly favorable response to first-order wave loads: The floating wind turbine rotates in pitch-direction about a point close to the rotor hub and the rotor fore-aft motion is almost unaffected by the wave excitation. As a result, the power production and the blade loads are not influenced by the waves. A comparable effect was so far known for Tension Leg Platforms but not for semi-submersible wind turbines. The methodology builds on a low-order simulation model, coupled to a parametric panel code model, a detailed viscous drag model and an individually tuned blade pitch controller. The results are confirmed by the higher-fidelity model FAST. A new indicator to express the optimal behavior through a single design criterion has been developed.
Secure Transmission for Intelligent Reflecting Surface-Assisted mmWave and Terahertz Systems
Venue: arXiv cs.IT 2020
Year: 2020

This letter focuses on the secure transmission for an intelligent reflecting surface (IRS)-assisted millimeter-wave (mmWave) and terahertz (THz) systems, in which a base station (BS) communicates with its destination via an IRS, in the presence of a passive eavesdropper. To maximize the system secrecy rate, the transmit beamforming at the BS and the reflecting matrix at the IRS are jointly optimized with transmit power and discrete phase-shift constraints. It is first proved that the beamforming design is independent of the phase shift design under the rank-one channel assumption. The formulated non-convex problem is then converted into two subproblems, which are solved alternatively. Specifically, the closed-form solution of transmit beamforming at the BS is derived, and the semidefinite programming (SDP)-based method and element-wise block coordinate descent (BCD)-based method are proposed to design the reflecting matrix. The complexity of our proposed methods is analyzed theoretically. Simulation results reveal that the proposed IRS-assisted secure strategy can significantly boost the secrecy rate performance, regardless of eavesdropper's locations (near or blocking the confidential beam).
Self-Training for Unsupervised Parsing with PRPN
Venue: arXiv cs.CL 2020
Year: 2020

Neural unsupervised parsing (UP) models learn to parse without access to syntactic annotations, while being optimized for another task like language modeling. In this work, we propose self-training for neural UP models: we leverage aggregated annotations predicted by copies of our model as supervision for future copies. To be able to use our model's predictions during training, we extend a recent neural UP architecture, the PRPN (Shen et al., 2018a) such that it can be trained in a semi-supervised fashion. We then add examples with parses predicted by our model to our unlabeled UP training data. Our self-trained model outperforms the PRPN by 8.1% F1 and the previous state of the art by 1.6% F1. In addition, we show that our architecture can also be helpful for semi-supervised parsing in ultra-low-resource settings.
Towards a Catalogue of Software Quality Metrics for Infrastructure Code
Venue: arXiv cs.SE 2020
Year: 2020

Infrastructure-as-code (IaC) is a practice to implement continuous deployment by allowing management and provisioning of infrastructure through the definition of machine-readable files and automation around them, rather than physical hardware configuration or interactive configuration tools. On the one hand, although IaC represents an ever-increasing widely adopted practice nowadays, still little is known concerning how to best maintain, speedily evolve, and continuously improve the code behind the IaC practice in a measurable fashion. On the other hand, source code measurements are often computed and analyzed to evaluate the different quality aspects of the software developed. However, unlike general-purpose programming languages (GPLs), IaC scripts use domain-specific languages, and metrics used for GPLs may not be applicable for IaC scripts. This article proposes a catalogue consisting of 46 metrics to identify IaC properties focusing on Ansible, one of the most popular IaC language to date, and shows how they can be used to analyze IaC scripts.
Omnis Prædictio: Estimating the Full Spectrum of Human Performance with Stroke Gestures
Venue: arXiv cs.HC 2020
Year: 2020

Designing effective, usable, and widely adoptable stroke gesture commands for graphical user interfaces is a challenging task that traditionally involves multiple iterative rounds of prototyping, implementation, and follow-up user studies and controlled experiments for evaluation, verification, and validation. An alternative approach is to employ theoretical models of human performance, which can deliver practitioners with insightful information right from the earliest stages of user interface design. However, very few aspects of the large spectrum of human performance with stroke gesture input have been investigated and modeled so far, leaving researchers and practitioners of gesture-based user interface design with a very narrow range of predictable measures of human performance, mostly focused on estimating production time, of which extremely few cases delivered accompanying software tools to assist modeling. We address this problem by introducing "Omnis Praedictio" (Omnis for short), a generic technique and companion web tool that provides accurate user-independent estimations of any numerical stroke gesture feature, including custom features specified in code. Our experimental results on three public datasets show that our model estimations correlate on average r > .9 with groundtruth data. Omnis also enables researchers and practitioners to understand human performance with stroke gestures on many levels and, consequently, raises the bar for human performance models and estimation techniques for stroke gesture input.
The Adversarial Resilience Learning Architecture for AI-based Modelling, Exploration, and Operation of Complex Cyber-Physical Systems
Venue: arXiv cs.AI 2020
Year: 2020

Modern algorithms in the domain of Deep Reinforcement Learning (DRL) demonstrated remarkable successes; most widely known are those in game-based scenarios, from ATARI video games to Go and the StarCraft~\textsc{II} real-time strategy game. However, applications in the domain of modern Cyber-Physical Systems (CPS) that take advantage a vast variety of DRL algorithms are few. We assume that the benefits would be considerable: Modern CPS have become increasingly complex and evolved beyond traditional methods of modelling and analysis. At the same time, these CPS are confronted with an increasing amount of stochastic inputs, from volatile energy sources in power grids to broad user participation stemming from markets. Approaches of system modelling that use techniques from the domain of Artificial Intelligence (AI) do not focus on analysis and operation. In this paper, we describe the concept of Adversarial Resilience Learning (ARL) that formulates a new approach to complex environment checking and resilient operation: It defines two agent classes, attacker and defender agents. The quintessence of ARL lies in both agents exploring the system and training each other without any domain knowledge. Here, we introduce the ARL software architecture that allows to use a wide range of model-free as well as model-based DRL-based algorithms, and document results of concrete experiment runs on a complex power grid.
GraFS: Graph Analytics Fusion and Synthesis
Venue: arXiv cs.DC 2020
Year: 2020

Graph analytics elicits insights from large graphs to inform critical decisions for business, safety and security. Several large-scale graph processing frameworks feature efficient runtime systems; however, they often provide programming models that are low-level and subtly different from each other. Therefore, end users can find implementation and specially optimization of graph analytics time-consuming and error-prone. This paper regards the abstract interface of the graph processing frameworks as the instruction set for graph analytics, and presents Grafs, a high-level declarative specification language for graph analytics and a synthesizer that automatically generates efficient code for five high-performance graph processing frameworks. It features novel semantics-preserving fusion transformations that optimize the specifications and reduce them to three primitives: reduction over paths, mapping over vertices and reduction over vertices. Reductions over paths are commonly calculated based on push or pull models that iteratively apply kernel functions at the vertices. This paper presents conditions, parametric in terms of the kernel functions, for the correctness and termination of the iterative models, and uses these conditions as specifications to automatically synthesize the kernel functions. Experimental results show that the generated code matches or outperforms hand-optimized code, and that fusion accelerates execution.
On motifs in colored graphs
Venue: arXiv cs.DM 2020
Year: 2020

One of the most important concepts in biological network analysis is that of network motifs, which are patterns of interconnections that occur in a given network at a frequency higher than expected in a random network. In this work we are interested in searching and inferring network motifs in a class of biological networks that can be represented by vertex-colored graphs. We show the computational complexity for many problems related to colorful topological motifs and present efficient algorithms for special cases. We also present a probabilistic strategy to detect highly frequent motifs in vertex-colored graphs. Experiments on real data sets show that our algorithms are very competitive both in efficiency and in quality of the solutions.
Using Hypotheses as a Debugging Aid
Venue: arXiv cs.SE 2020
Year: 2020

As developers debug, developers formulate hypotheses about the cause of the defect and gather evidence to test these hypotheses. To better understand the role of hypotheses in debugging, we conducted two studies. In a preliminary study, we found that, even with the benefit of modern internet resources, incorrect hypotheses can cause developers to investigate irrelevant information and block progress. We then conducted a controlled experiment where 20 developers debugged and recorded their hypotheses. We found that developers have few hypotheses, two per defect. Having a correct hypothesis early strongly predicted later success. We also studied the impact of two debugging aids: fault locations and potential hypotheses. Offering fault locations did not help developers formulate more correct hypotheses or debug more successfully. In contrast, offering potential hypotheses made developers six times more likely to succeed. These results demonstrate the potential of future debugging tools that enable finding and sharing relevant hypotheses.
BeCAPTCHA: Behavioral Bot Detection using Touchscreen and Mobile Sensors benchmarked on HuMIdb
Venue: arXiv cs.CR 2020
Year: 2020

In this paper we study the suitability of a new generation of CAPTCHA methods based on smartphone interactions. The heterogeneous flow of data generated during the interaction with the smartphones can be used to model human behavior when interacting with the technology and improve bot detection algorithms. For this, we propose BeCAPTCHA, a CAPTCHA method based on the analysis of the touchscreen information obtained during a single drag and drop task in combination with the accelerometer data. The goal of BeCAPTCHA is to determine whether the drag and drop task was realized by a human or a bot. We evaluate the method by generating fake samples synthesized with Generative Adversarial Neural Networks and handcrafted methods. Our results suggest the potential of mobile sensors to characterize the human behavior and develop a new generation of CAPTCHAs. The experiments are evaluated with HuMIdb (Human Mobile Interaction database), a novel multimodal mobile database that comprises 14 mobile sensors acquired from 600 users. HuMIdb is freely available to the research community.
Detecting Scatteredly-Distributed, Small, andCritically Important Objects in 3D OncologyImaging via Decision Stratification
Venue: arXiv cs.CV 2020
Year: 2020

Finding and identifying scatteredly-distributed, small, and critically important objects in 3D oncology images is very challenging. We focus on the detection and segmentation of oncology-significant (or suspicious cancer metastasized) lymph nodes (OSLNs), which has not been studied before as a computational task. Determining and delineating the spread of OSLNs is essential in defining the corresponding resection/irradiating regions for the downstream workflows of surgical resection and radiotherapy of various cancers. For patients who are treated with radiotherapy, this task is performed by experienced radiation oncologists that involves high-level reasoning on whether LNs are metastasized, which is subject to high inter-observer variations. In this work, we propose a divide-and-conquer decision stratification approach that divides OSLNs into tumor-proximal and tumor-distal categories. This is motivated by the observation that each category has its own different underlying distributions in appearance, size and other characteristics. Two separate detection-by-segmentation networks are trained per category and fused. To further reduce false positives (FP), we present a novel global-local network (GLNet) that combines high-level lesion characteristics with features learned from localized 3D image patches. Our method is evaluated on a dataset of 141 esophageal cancer patients with PET and CT modalities (the largest to-date). Our results significantly improve the recall from $45\%$ to $67\%$ at $3$ FPs per patient as compared to previous state-of-the-art methods. The highest achieved OSLN recall of $0.828$ is clinically relevant and valuable.
Tensor Decomposition for Multi-agent Predictive State Representation
Venue: arXiv cs.LG 2020
Year: 2020

Predictive state representation~(PSR) uses a vector of action-observation sequence to represent the system dynamics and subsequently predicts the probability of future events. It is a concise knowledge representation that is well studied in a single-agent planning problem domain. To the best of our knowledge, there is no existing work on using PSR to solve multi-agent planning problems. Learning a multi-agent PSR model is quite difficult especially with the increasing number of agents, not to mention the complexity of a problem domain. In this paper, we resort to tensor techniques to tackle the challenging task of multi-agent PSR model development problems. By first focusing on a two-agent setting, we construct the system dynamics matrix as a high order tensor for a PSR model, learn the prediction parameters and deduce state vectors directly through two different tensor decomposition methods respectively, and derive the transition parameters via linear regression. Subsequently, we generalize the PSR learning approaches in a multi-agent setting. Experimental results show that our methods can effectively solve multi-agent PSR modelling problems in multiple problem domains.
Better and Simpler Learning-Augmented Online Caching
Venue: arXiv cs.DS 2020
Year: 2020

Lykouris and Vassilvitskii (ICML 2018) introduce a model of online caching with machine-learned advice, where each page request additionally comes with a prediction of when that page will next be requested. In this model, a natural goal is to design algorithms that (1) perform well when the advice is accurate and (2) remain robust in the worst case a la traditional competitive analysis. Lykouris and Vassilvitskii give such an algorithm by adapting the Marker algorithm to the learning-augmented setting. In a recent work, Rohatgi (SODA 2020) improves on their result with an approach also inspired by randomized marking. We continue the study of this problem, but with a somewhat different approach: We consider combining the BlindOracle algorithm, which just naïvely follows the predictions, with an optimal competitive algorithm for online caching in a black-box manner. The resulting algorithm outperforms all existing approaches while being significantly simpler. Moreover, we show that combining BlindOracle with LRU is in fact optimal among deterministic algorithms for this problem.
Multi-access Coded Caching Schemes From Cross Resolvable Designs
Venue: arXiv cs.IT 2020
Year: 2020

We present a novel caching and coded delivery scheme for a multi-access network where multiple users can have access to the same cache (shared cache) and any cache can assist multiple users. This scheme is obtained from resolvable designs satisfying certain conditions which we call {\it cross resolvable designs}. To be able to compare different multi-access coded schemes with different number of users we normalize the rate of the schemes by the number of users served. Based on this per-user-rate we show that our scheme performs better than the well known Maddah-Ali - Nieson (MaN) scheme and the recently proposed ("Multi-access coded caching : gains beyond cache-redundancy" by Serbetci, Parrinello and Elia) SPE scheme. It is shown that the resolvable designs from affine planes are cross resolvable designs and our scheme based on these performs better than the MaN scheme for large memory size cases. The exact size beyond which our performance is better is also presented. The SPE scheme considers only the cases where the product of the number of users and the normalized cache size is 2, whereas the proposed scheme allows different choices depending on the choice of the cross resolvable design.
Logic Verification of Ultra-Deep Pipelined Beyond-CMOS Technologies
Venue: arXiv cs.ET 2020
Year: 2020

Traditional logical equivalence checking (LEC) which plays a major role in entire chip design process faces challenges of meeting the requirements demanded by the many emerging technologies that are based on logic models different from standard complementary metal oxide semiconductor (CMOS). In this paper, we propose a LEC framework to be employed in the verification process of beyond-CMOS circuits. Our LEC framework is compatible with existing CMOS technologies, but, also able to check features and capabilities that are unique to beyond-CMOS technologies. For instance, the performance of some emerging technologies benefits from ultra-deep pipelining and verification of such circuits requires new models and algorithms. We, therefore, present the Multi-Cycle Input Dependency (MCID) circuit model which is a novel model representation of design to explicitly capture the dependency of primary outputs of the circuit on sequences of internal signals and inputs. Embedding the proposed circuit model and several structural checking modules, the process of verification can be independent of the underlying technology and signaling. We benchmark the proposed framework on post-synthesis rapid single-flux-quantum (RSFQ) netlists. Results show a comparative verification time of RSFQ circuit benchmark including 32-bit Kogge-Stone adder, 16-bit integer divider, and ISCAS'85 circuits with respect to ABC tool for similar CMOS circuits.
Complex networks for event detection in heterogeneous high volume news streams
Venue: arXiv cs.SI 2020
Year: 2020

Detecting important events in high volume news streams is an important task for a variety of purposes.The volume and rate of online news increases the need for automated event detection methods thatcan operate in real time. In this paper we develop a network-based approach that makes the workingassumption that important news events always involve named entities (such as persons, locationsand organizations) that are linked in news articles. Our approach uses natural language processingtechniques to detect these entities in a stream of news articles and then creates a time-stamped seriesof networks in which the detected entities are linked by co-occurrence in articles and sentences. Inthis prototype, weighted node degree is tracked over time and change-point detection used to locateimportant events. Potential events are characterized and distinguished using community detectionon KeyGraphs that relate named entities and informative noun-phrases from related articles. Thismethodology already produces promising results and will be extended in future to include a widervariety of complex network analysis techniques.